Practical overview
When it fits
Use Rllm when an agentic workflow needs a documented path from rollout collection and reward evaluation to reinforcement-learning updates, especially when the harness, sandbox or training backend may change.
Who it is for
- Researchers experimenting with reinforcement learning for language agents.
- Engineering teams building coding, terminal, search or other agentic workflows.
- Developers who want the same agent flow to support evaluation and training.
- Teams comparing multiple training backends or benchmark suites.
Documented uses
- Train an LLM-based agent with reward-driven updates.
- Collect structured traces from agent runs.
- Evaluate agent systems across integrated benchmarks.
- Adapt an existing agent harness to reinforcement-learning experiments.
- Run agent rollouts through local or hosted sandbox options documented by the project.
Requirements & limits
The repository is public, open source and licensed under Apache-2.0. The documented setup requires Python 3.11 or newer and supports CLI and self-hosted workflows on Linux, macOS and Windows.
- Choose the base installation for the documented tinker single-machine backend; use the separate verl or Fireworks installation extras for those backends.
- The framework supports multiple harnesses and custom wrappers, but custom integrations require an agent flow that follows Rllm’s rollout and episode interfaces.
- Benchmark coverage is broad, with the repository documenting more than 60 integrated benchmarks and an automatic evaluation command.
- The training pipeline depends on a reward or evaluator definition, so the quality of the training signal is part of the implementation work.
- Sandbox selection affects the rollout environment: the documented options include Docker, Daytona, Modal and local execution.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Connect a custom agent flow to Rllm’s Python trainer
Define a rollout and evaluator, then pass them to AgentTrainer with a training dataset.
Before you start
- Python 3.11 or newer.
- A Python agent flow that returns an Rllm Episode.
- An evaluator that returns an EvalOutput with a reward.
- A trainer configuration and training dataset.
- An installed Rllm environment and selected training backend.
Steps
Define the agent with the documented rollout interface and return an Episode containing the agent output.
Define an evaluator that reads the episode and returns a reward and evaluation signals.
Create AgentTrainer with the selected backend, agent flow, evaluator, configuration and training dataset.
Call the trainer’s documented train method.
Expected outcome
The custom agent flow and reward function are connected to Rllm’s training pipeline.
Guide limitations
- The supplied implementation example shows the interface and trainer construction but does not provide a complete dataset or backend deployment procedure.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 3 October 2026
- Original website title
- rllm-org/rllm · Official homepage title · retained in its source language